General Information
Using an information systems approach, this subject outlines the design principles and techniques necessary to produce appropriate infrastructure specifications for different data analytic systems. These requirements can be specified in terms of people, procedures, data, software, and hardware. Successful designs will allow systems to automatically extract insights from vast amounts of available data. Topics include, but are not limited to, key modern issues such as job roles in data analytic ecosystems, the operation of organisations, security and data integrity principles, business processes, blockchains, NoSQL databases, cloud solutions, software options and fundamental tenets of computing. The knowledge of these, and understanding how the components interact together, allow students to design efficient systems that are robust to change and conform to best practice.
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Details
Academic unit: Bond Business School Subject code: DTSC13-300 Subject title: Infrastructure for Data Analytics Subject level: Undergraduate Semester/Year: September 2024 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Forum: x12 (Total hours: 24) - Forum
- Computer Lab: x12 (Total hours: 24) - Computer Lab
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. +++++ BBS uses a self and peer-evaluation system to support students engaged in group-based assessments. Students are expected to provide this feedback in a timely fashion as part of their assessment. The information gathered is used by the educator as partial evidence of equitable contributions by all group members and helps to determine individual marks for group assessments. -
Resources
Prescribed resources: No Prescribed resources.
After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.iLearn@Bond & Email: iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications.
Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.
To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
| Academic unit: | Bond Business School |
|---|---|
| Subject code: | DTSC13-300 |
| Subject title: | Infrastructure for Data Analytics |
| Subject level: | Undergraduate |
| Semester/Year: | September 2024 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
|
| Attendance and learning activities: | Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. +++++ BBS uses a self and peer-evaluation system to support students engaged in group-based assessments. Students are expected to provide this feedback in a timely fashion as part of their assessment. The information gathered is used by the educator as partial evidence of equitable contributions by all group members and helps to determine individual marks for group assessments. |
| Prescribed resources: | No Prescribed resources. After enrolment, students can check the Books and Tools area in iLearn for the full Resource List. |
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au |
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent):Possess demonstrable knowledge in elementary probability theory, statistics, elementary calculus and linear algebra to the level of a unit such as STAT11-112 Quantitative Methods. |
| Restrictions: |
Nil |
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Apply an information systems framework to determine the type of infrastructure requirements needed for basic data analytic systems.
- Demonstrate the ability to implement basic prototype deployments for big data analytics
- Describe key technical aspects of people, procedures, data, software and hardware as they pertain to data analytic information systems.
- Compare the advantages and disadvantages of basic infrastructure options for data analytic projects.
- Evaluate basic business data infrastructure issues using relevant concepts, models and theories.
- Express business information in a clear, concise writing style tailored to a general audience.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed Computer-aided Test (Open) In-class test of student understanding of the materials presented to date. 15.00% Week 6 1,2,3 Computer-aided Test (Open) In-class test of student understanding of the materials presented to date. 15.00% Week 12 1,2,3 Written Report Students will firstly be required to produce an infrastructure consultant’s report based on a real-world derived scenario. They will need to analyse a data analytic problem in terms of existing infrastructure and, using the infrastructure design principles, recommend appropriate infrastructure that aligns with industry best practice. 50.00% Week 12 1,2,3,4,5,6 Student Engagement Participation 20.00% Ongoing 2,4 - * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
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Assessment criteria
Assessment criteria
High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment. Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| Computer-aided Test (Open) | In-class test of student understanding of the materials presented to date. | 15.00% | Week 6 | 1,2,3 |
| Computer-aided Test (Open) | In-class test of student understanding of the materials presented to date. | 15.00% | Week 12 | 1,2,3 |
| Written Report | Students will firstly be required to produce an infrastructure consultant’s report based on a real-world derived scenario. They will need to analyse a data analytic problem in terms of existing infrastructure and, using the infrastructure design principles, recommend appropriate infrastructure that aligns with industry best practice. | 50.00% | Week 12 | 1,2,3,4,5,6 |
| Student Engagement | Participation | 20.00% | Ongoing | 2,4 |
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
Assessment criteria
| High Distinction | 85-100 | Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. |
|---|---|---|
| Distinction | 75-84 | Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. |
| Credit | 65-74 | Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. |
| Pass | 50-64 | Usually awarded to students whose performance meets the requirements set for work provided for assessment. |
| Fail | 0-49 | Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. |
Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Study Information
Submission procedures
Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
A late penalty will be applied to all overdue assessment tasks unless the Lead Educator grants an extension. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.
Academic Integrity
Bond University‘s Student Code of Conduct Policy , Student Charter, Academic Integrity Policy and our Graduate Attributes guide expectations regarding student behaviour, their rights and responsibilities. Information on these topics can be found on our Academic Integrity webpage recognising that academic integrity involves demonstrating the principles of integrity (honesty, fairness, trust, professionalism, courage, responsibility, and respect) in words and actions across all aspects of academic endeavour.
Staff are required to report suspected misconduct. This includes all types of plagiarism, cheating, collusion, fabrication or falsification of data/content or other misconduct relating to assessment such as the falsification of medical certificates for assessment extensions. The longer term personal, social and financial consequences of misconduct can be severe, so please ask for help if you are unsure.
If your work is subject to an inquiry, you will be given an opportunity to respond and appropriate support will be provided. Academic work under inquiry will not be marked until the process has concluded. Penalties for misconduct include a warning, reduced grade, a requirement to repeat the assessment, suspension or expulsion from the University.
Feedback on assessment
Feedback on assessment will be provided to students according to the requirements of the Assessment Procedure Schedule A - Assessment Communication Procedure.
Whilst in most cases feedback should be provided within two weeks of the assessment submission due date, the Procedure should be checked if the assessment is linked to others or if the subject is a non-standard (e.g., intensive) subject.
Accessibility and Inclusion Support
Support is available to students where a physical, mental or neurological condition exists that would impact the student’s capacity to complete studies, exams or assessment tasks. For effective support, special requirement needs should be arranged with the University in advance of or at the start of each semester, or, for acute conditions, as soon as practicable after the condition arises. Reasonable adjustments are not guaranteed where applications are submitted late in the semester (for example, when lodged just prior to critical assessment and examination dates).
As outlined in the Accessibility and Inclusion Policy, to qualify for support, students must meet certain criteria. Students are also required to meet with the Accessibility and Inclusion Advisor who will ensure that reasonable adjustments are afforded to qualifying students.
For more information and to apply online, visit BondAbility.
Additional subject information
As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Subject curriculum
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Introduction to data analytic Infrastructure with an information systems approach
Data analytic systems are now used as a standard part of business operations. These systems require appropriate infrastructure to operate correctly and efficiently. We define infrastructure categories using a classical information systems approach.
SLOs included
- Apply an information systems framework to determine the type of infrastructure requirements needed for basic data analytic systems.
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Data: Database implementations and frameworks, and a history of data storage
Paper and electronic databases have been an important part of businesses for many decades. The rise of data analytics has meant that traditional relational approaches need to be supplemented by emerging NoSQL style databases. These appropriately capture increasingly important semi-structured and non-structured data.
SLOs included
- Describe key technical aspects of people, procedures, data, software and hardware as they pertain to data analytic information systems.
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Hardware, Software: Cloud based solutions and a survey of applications
The cloud gives public and company accessibility to a large amount of hardware and software infrastructure. The key issues including types of resources and security issues are discussed.
SLOs included
- Demonstrate the ability to implement basic prototype deployments for big data analytics
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Hardware: Computing methods and different types of hardware
The base of any digital information system is hardware. The five different categories of hardware are discussed in relation to the needs of data analytic systems.
SLOs included
- Express business information in a clear, concise writing style tailored to a general audience.
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People and Procedures: Security and encryption
One of the largest issues in any digital system, including data analytic systems, is security. Common security threats as well as measures to protect sensitive data are discussed.
SLOs included
- Compare the advantages and disadvantages of basic infrastructure options for data analytic projects.
- Evaluate basic business data infrastructure issues using relevant concepts, models and theories.
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Software: Parallel computing and big data tools
Given the growth of data that requires analysis, parallel resources are needed to process data, particularly for real time applications and continuous data. Paradigms such as MapReduce and tools like Hadoop and Spark are explored. Both coarse and fine grain parallelism techniques and metrics are given.
SLOs included
- Apply an information systems framework to determine the type of infrastructure requirements needed for basic data analytic systems.
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People and Procedures: Different job types and roles in big data in organisations and government
People are the most important part of any system. The organisation of people, in either traditional company structures and emerging flat and matrix structures, is discussed along with the advantages and disadvantages of each. The different job roles in data analytic system ecosystems are also explored.
SLOs included
- Express business information in a clear, concise writing style tailored to a general audience.
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People and Procedures: Algorithms, pseudocode and business processes
The documentation of data analytic systems is incredibly important to ensure that users and developers have an accurate understanding of them. Three different aspects of this topic, algorithms, pseudocode, and business processes are explored. The tools of efficiency analysis and business process modelling notation are introduced.
SLOs included
- Express business information in a clear, concise writing style tailored to a general audience.
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Data: Blockchains
Blockchains are now being used to securely store and manipulate continuous streams of data. Topics such as their use in digital currency and data mining are explored.
SLOs included
- Apply an information systems framework to determine the type of infrastructure requirements needed for basic data analytic systems.
- Express business information in a clear, concise writing style tailored to a general audience.
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People and Procedures: Privacy and ethics
In data driven systems, breaches of privacy and unethical use of data, are important considerations. Both potential threats and the design of mitigation measures are discussed. Different ethical frameworks are also considered.
SLOs included
- Express business information in a clear, concise writing style tailored to a general audience.
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Emerging Trends and subject summary
Data analytic systems rapidly change over time given advances in technology. New technologies relevant to future infrastructure needs are considered. Additionally, a summary of all aspects of infrastructure are covered in this final topic.
SLOs included
- Apply an information systems framework to determine the type of infrastructure requirements needed for basic data analytic systems.
- Demonstrate the ability to implement basic prototype deployments for big data analytics
- Describe key technical aspects of people, procedures, data, software and hardware as they pertain to data analytic information systems.
- Compare the advantages and disadvantages of basic infrastructure options for data analytic projects.
- Evaluate basic business data infrastructure issues using relevant concepts, models and theories.
- Express business information in a clear, concise writing style tailored to a general audience.